Applied Scientist: ML, Causal Inference & Modelling

Roc Search; Greater London; On-site; GBP 70,000 - 110,000; Full time

Job summary

Roc Search is seeking an Applied Scientist specialising in Machine Learning, Causal Inference and Mathematical Modelling. You will work across the full ML lifecycle, from research to deployment, using statistical methods and causal reasoning to solve complex real-world systems.

You will collaborate with engineers and domain experts to translate problems into mathematical models and deliver practical AI applications that balance research with engineering impact.

Qualifications

• PhD or MSc in a quantitative field required.
• Proven experience with ML, statistical modelling or applied maths research.
• Strong Python programming skills with PyTorch, JAX or scikit-learn.
• Experience with causal inference, experimental design or statistical analysis.

Responsibilities

• Develop ML, statistical and causal inference methods for real-world problems.
• Design experiments to identify causal relationships and test hypotheses.
• Translate problems into mathematical and computational models.
• Analyse time-series and observational data to identify patterns.
• Develop predictive and decision-making models using modern ML techniques.
• Apply simulation and mathematical modelling to complex systems.
• Collaborate with engineers to deploy models into applications.
• Contribute to research and development of new methodologies.

Skills

Python programming

ML frameworks (PyTorch, JAX, scikit-le

Statistical modelling

Causal inference

Time-series analysis

Mathematical modelling

Problem solving

Education

PhD or MSc in Statistics, Applied Mathematics, Machine Learning, Engineering, Physics or related discipline

Degree in Mathematics, Statistics, Physics, Engineering, Computer Science or another quantitative discipline

Tools

PyTorch

JAX

scikit-learn

Job description

Roc Search is seeking an Applied Scientist specialising in Machine Learning, Causal Inference and Mathematical Modelling. You will work across the full ML lifecycle, from research to deployment, using statistical methods and causal reasoning to solve complex real-world systems.

You will collaborate with engineers and domain experts to translate problems into mathematical models and deliver practical AI applications that balance research with engineering impact.